Paper Citation Record · LEDGER

LLMaAA: Making Large Language Models as Active Annotators

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2310.19596.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2310.19596 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:05:32.617230Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-21T20:50:36.534244Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 40f8b30f-9688-49c2-927f-f6c13d21e4d6 · inbound

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods cites this paper.

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods LLMaAA: Making Large Language Models as Active Annotators

Reference 293

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:08:35.931863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9124d1d2-e07c-4dbd-b29e-f3c1f3a71010 · inbound

Can AI Extract Antecedent Factors of Human Trust in AI? An Application of Information Extraction for Scientific Literature in Behavioural and Computer Sciences cites this paper.

Can AI Extract Antecedent Factors of Human Trust in AI? An Application of Information Extraction for Scientific Literature in Behavioural and Computer Sciences LLMaAA: Making Large Language Models as Active Annotators

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T15:05:32.617230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76b94dd9-df7b-45f0-93f8-e27988fb705f · inbound

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection cites this paper.

ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection LLMaAA: Making Large Language Models as Active Annotators

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T21:53:06.330433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f22bc1e0-d7fa-4bd8-9ba3-941f36e36b57 · inbound

Evaluating Large Language Models as Expert Annotators cites this paper.

Evaluating Large Language Models as Expert Annotators LLMaAA: Making Large Language Models as Active Annotators

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-05T21:58:02.659226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fa23572-8b6e-4eac-bccd-62e4c9a3ae38 · inbound

PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data cites this paper.

PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data LLMaAA: Making Large Language Models as Active Annotators

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-05T18:08:44.843029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c157e29-a430-4541-b635-b0c7ded25257 · inbound

Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains cites this paper.

Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains LLMaAA: Making Large Language Models as Active Annotators

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-05T17:02:40.551517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d558a0f0-e019-4070-aca9-ae307ce3b304 · inbound

Towards Consistent Detection of Cognitive Distortions: LLM-Based Annotation and Dataset-Agnostic Evaluation cites this paper.

Towards Consistent Detection of Cognitive Distortions: LLM-Based Annotation and Dataset-Agnostic Evaluation LLMaAA: Making Large Language Models as Active Annotators

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:50:36.536370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1205d0b6-b9fd-4ef7-a98b-1659c3422aae · inbound

A Patient Simulation Framework for Risk Assessment of Conversational Healthcare AI: Evaluation of an Antidepressant Decision Aid cites this paper.

A Patient Simulation Framework for Risk Assessment of Conversational Healthcare AI: Evaluation of an Antidepressant Decision Aid LLMaAA: Making Large Language Models as Active Annotators

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-03T00:13:38.699173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d16cec5-34fd-4d47-9055-dca8db8ebb78 · inbound

Can We Trust a Black-box LLM? LLM Untrustworthy Boundary Detection via Bias-Diffusion and Multi-Agent Reinforcement Learning cites this paper.

Can We Trust a Black-box LLM? LLM Untrustworthy Boundary Detection via Bias-Diffusion and Multi-Agent Reinforcement Learning LLMaAA: Making Large Language Models as Active Annotators

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:30:51.265739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 80067014-99d2-40f3-929f-acb6d9d6e860 · inbound

Structured Exploration and Exploitation of Label Functions for Automated Data Annotation cites this paper.

Structured Exploration and Exploitation of Label Functions for Automated Data Annotation LLMaAA: Making Large Language Models as Active Annotators

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-14T23:28:17.080675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2283a2f7-b13a-43bc-8104-72d6819679fa · inbound

A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research cites this paper.

A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research LLMaAA: Making Large Language Models as Active Annotators

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-20T17:53:36.462108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e651139f-aa4f-4d27-9c5b-efa58ac8bef6 · inbound

DE-NER : Zero-shot Named Entity Recognition via Dialogue Elicitation of Large Language Models cites this paper.

DE-NER : Zero-shot Named Entity Recognition via Dialogue Elicitation of Large Language Models LLMaAA: Making Large Language Models as Active Annotators

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T00:45:14.982875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.